AIC average by age group

Run regressions between model parameters and age

## 
## Call:
## lm(formula = LL ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -154.578  -58.515    9.859   54.104  200.722 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -215.354     19.598  -10.99   <2e-16 ***
## age            1.591      1.170    1.36    0.176    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 72.28 on 148 degrees of freedom
## Multiple R-squared:  0.01235,    Adjusted R-squared:  0.005673 
## F-statistic:  1.85 on 1 and 148 DF,  p-value: 0.1758
## 
## Call:
## lm(formula = alphaPosChoice ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3090 -0.1999 -0.0917  0.1265  0.6863 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.320756   0.074344   4.314 2.91e-05 ***
## age         -0.001369   0.004438  -0.308    0.758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2742 on 148 degrees of freedom
## Multiple R-squared:  0.0006424,  Adjusted R-squared:  -0.00611 
## F-statistic: 0.09514 on 1 and 148 DF,  p-value: 0.7582
## 
## Call:
## lm(formula = alphaNegChoice ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20679 -0.12581 -0.06585  0.00345  0.81559 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.304868   0.061239   4.978 1.76e-06 ***
## age         -0.011916   0.003656  -3.260  0.00138 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2259 on 148 degrees of freedom
## Multiple R-squared:  0.06698,    Adjusted R-squared:  0.06068 
## F-statistic: 10.62 on 1 and 148 DF,  p-value: 0.001385
## 
## Call:
## lm(formula = alphaPosComp ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23170 -0.17898 -0.12042  0.05715  0.87437 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.300954   0.073408   4.100  6.8e-05 ***
## age         -0.007638   0.004382  -1.743   0.0834 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2707 on 148 degrees of freedom
## Multiple R-squared:  0.02012,    Adjusted R-squared:  0.01349 
## F-statistic: 3.038 on 1 and 148 DF,  p-value: 0.08341
## 
## Call:
## lm(formula = alphaNegComp ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2432 -0.2170 -0.1918  0.2049  0.7903 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.270545   0.088504   3.057  0.00265 **
## age         -0.003096   0.005284  -0.586  0.55877   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3264 on 148 degrees of freedom
## Multiple R-squared:  0.002315,   Adjusted R-squared:  -0.004426 
## F-statistic: 0.3434 on 1 and 148 DF,  p-value: 0.5588
## 
## Call:
## lm(formula = betaAgency ~ age, data = model_params)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.158 -4.012 -1.747  2.933 20.338 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  3.53843    1.51875   2.330  0.02117 * 
## age          0.24547    0.09067   2.707  0.00758 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.601 on 148 degrees of freedom
## Multiple R-squared:  0.04719,    Adjusted R-squared:  0.04075 
## F-statistic:  7.33 on 1 and 148 DF,  p-value: 0.007579
## 
## Call:
## lm(formula = betaMachine ~ age, data = model_params)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.833 -3.151 -1.048  1.911 22.856 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4.9326     1.3383   3.686 0.000319 ***
## age           0.1402     0.0799   1.755 0.081273 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.936 on 148 degrees of freedom
## Multiple R-squared:  0.02039,    Adjusted R-squared:  0.01378 
## F-statistic: 3.081 on 1 and 148 DF,  p-value: 0.08127
## 
## Call:
## lm(formula = agencyBonus ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.72271 -0.30021 -0.19730  0.08013  2.37437 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.72607    0.18718   3.879 0.000157 ***
## age         -0.01312    0.01117  -1.174 0.242346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6904 on 148 degrees of freedom
## Multiple R-squared:  0.009224,   Adjusted R-squared:  0.00253 
## F-statistic: 1.378 on 1 and 148 DF,  p-value: 0.2423

Learning rate model

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: estimate ~ age_z * valence * agency + (1 | subject_id)
## Data: learning_rates
##                 Effect        df         F p.value
## 1                age_z 1, 148.00    5.38 *    .022
## 2              valence 1, 444.00 11.17 ***   <.001
## 3               agency 1, 444.00      0.10    .753
## 4        age_z:valence 1, 444.00      0.51    .477
## 5         age_z:agency 1, 444.00      0.09    .762
## 6       valence:agency 1, 444.00 28.34 ***   <.001
## 7 age_z:valence:agency 1, 444.00    3.20 +    .074
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ age_z * valence * agency + (1 | subject_id)
##    Data: data
## 
## REML criterion at convergence: 199.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5327 -0.6127 -0.3271  0.2431  3.2261 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_id (Intercept) 0.008624 0.09287 
##  Residual               0.067885 0.26055 
## Number of obs: 600, groups:  subject_id, 150
## 
## Fixed effects:
##                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              0.203361   0.013063 148.000000  15.568  < 2e-16 ***
## age_z                   -0.030316   0.013074 148.000000  -2.319   0.0218 *  
## valence1                -0.035555   0.010637 444.000000  -3.343   0.0009 ***
## agency1                  0.003345   0.010637 444.000000   0.314   0.7533    
## age_z:valence1          -0.007579   0.010646 444.000000  -0.712   0.4769    
## age_z:agency1           -0.003219   0.010646 444.000000  -0.302   0.7625    
## valence1:agency1        -0.056629   0.010637 444.000000  -5.324 1.62e-07 ***
## age_z:valence1:agency1  -0.019045   0.010646 444.000000  -1.789   0.0743 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age_z valnc1 agncy1 ag_z:v1 ag_z:g1 vln1:1
## age_z       0.000                                            
## valence1    0.000  0.000                                     
## agency1     0.000  0.000 0.000                               
## age_z:vlnc1 0.000  0.000 0.000  0.000                        
## age_z:gncy1 0.000  0.000 0.000  0.000  0.000                 
## vlnc1:gncy1 0.000  0.000 0.000  0.000  0.000   0.000         
## ag_z:vln1:1 0.000  0.000 0.000  0.000  0.000   0.000   0.000
## 
##  Paired t-test
## 
## data:  model_params$alphaPosChoice and model_params$alphaNegChoice
## t = 6.9666, df = 149, p-value = 9.73e-11
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  0.1320728 0.2366607
## sample estimates:
## mean difference 
##       0.1843667
## 
##  Paired t-test
## 
## data:  model_params$alphaPosComp and model_params$alphaNegComp
## t = -1.1108, df = 149, p-value = 0.2685
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.11712714  0.03283236
## sample estimates:
## mean difference 
##     -0.04214739

Plot relations between model parameters and age

---
title: "E2 VoC Analyses Part 3: RL Analyses"
date: 1/8/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load libraries, include = F}

#load libraries
library(tidyverse)
library(glue)
library(afex)
library(latex2exp)

#load scripts
source('analysis_scripts/voc_functions.R')
```

```{r import data}
# read in participant ages
participant_ages <- read_csv('data/voc_sub_info.csv') 

## read in aics
aics = read_csv("RL_modeling/output/aics_all_16_models_100iter.csv") %>%
  rename(subject_id = subID)

# combine with ages
aics <- inner_join(aics, participant_ages, by = 'subject_id') %>%
  mutate(age_group = case_when(age < 13 ~ "Children",
                               age > 12.99 & age < 18 ~ "Adolescents",
                               age > 17.99 ~ "Adults"))

aics$age_group <- factor(aics$age_group, levels = c("Children", "Adolescents", "Adults"))
         

#pivot longer
model_results <- pivot_longer(aics, 
                      cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                      names_to = "model",
                      values_to = "AIC")


model_results$model <- factor(model_results$model, 
                              levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta",
                                         "oneAlpha_oneBeta_agencyBonus",
                                         "oneAlpha_twoBeta_agencyBonus",
                                         "twoAlpha_oneBeta_agencyBonus",
                                         "twoAlpha_twoBeta_agencyBonus",
                                         "twoAlphaValenced_oneBeta_agencyBonus",
                                         "twoAlphaValenced_twoBeta_agencyBonus",
                                         "fourAlpha_oneBeta_agencyBonus",
                                         "fourAlpha_twoBeta_agencyBonus"))
model_results <- model_results %>%
  mutate(agencyBonus = case_when(str_detect(model, "agency") ~ "With Agency Bonus",
                                 !str_detect(model, "agency") ~ "No Agency Bonus"),
         shortName = str_remove(model, '_agencyBonus'))

model_results$shortName <- factor(model_results$shortName,
                                  levels = c("oneAlpha_oneBeta",
                                             "oneAlpha_twoBeta",
                                             "twoAlpha_oneBeta",
                                             "twoAlpha_twoBeta",
                                             "twoAlphaValenced_oneBeta",
                                             "twoAlphaValenced_twoBeta",
                                             "fourAlpha_oneBeta",
                                             "fourAlpha_twoBeta"))
```

#  AIC average by age group 
```{r plot AIC by age group, fig.width = 8, fig.height = 5, units = "in"}
#summarize
model_summary <- model_results %>%
  group_by(age_group, shortName, agencyBonus) %>%
  summarize(mean_aic = mean(AIC))

## Plot the results by age group 
AIC_age_plot <- ggplot(model_summary, aes(x = age_group, y = mean_aic, fill = shortName))+
  facet_wrap(~agencyBonus) +
  geom_bar(stat = "identity", position = "dodge", color = "black") +
  scale_fill_manual(name = "Model",
                    values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                    labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
  coord_cartesian(ylim = c(350, 650)) +
  ylab("Mean AIC") +
  xlab("") +
  voc_theme() +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))
AIC_age_plot
```


#  Examine age-related change in parameter estimates from models
```{r load parameters from winning model}
model_params <- read_csv("RL_modeling/output/model_fits_real_data/fourAlpha_twoBeta_agencyBonus.csv",
                         col_names = c("negLL",
                                       "logPost",
                                       "AIC",
                                       "BIC",
                                       "alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"))

#add sub ID and information
subject_id <- model_results %>% select(subject_id) %>% unique()
model_params <- bind_cols(subject_id, model_params)
model_params <- inner_join(participant_ages, model_params, by = c("subject_id"))

```


# Run regressions between model parameters and age
```{r parameter regressions}
model_params$LL <- model_params$negLL * -1

# Log likelihood
summary(lm(LL ~ age, data = model_params))
# not significant

# Alpha Pos Choice
summary(lm(alphaPosChoice ~ age, data = model_params))
#not significant

# Alpha Neg Choice
summary(lm(alphaNegChoice ~ age, data = model_params))
# significant

# Alpha Pos Comp
summary(lm(alphaPosComp ~ age, data = model_params))
#not significant

# Alpha Neg Comp
summary(lm(alphaNegComp ~ age, data = model_params))
#not significant

# Beta Agency
summary(lm(betaAgency ~ age, data = model_params))
# significant

# Beta Bandit
summary(lm(betaMachine ~ age, data = model_params))
# not significant

# agency bonus
summary(lm(agencyBonus ~ age, data = model_params))
# not significant

```

# Learning rate model
```{r learning rate regression}
## Learning rate model
learning_rates <- model_params %>%
  pivot_longer(cols = c(alphaPosChoice:alphaNegComp),
               names_to = "learningRate",
               values_to = "estimate") %>%
  select(subject_id, age, learningRate, estimate) %>%
  unique() %>%
  mutate(valence = case_when(str_detect(learningRate, "Pos") ~ "Positive",
                             str_detect(learningRate, "Neg") ~ "Negative"),
         agency = case_when(str_detect(learningRate, "Choice") ~ "Choice",
                            str_detect(learningRate, "Comp") ~ "Comp"))

learning_rates$age_z <- scale_this(learning_rates$age)

learning_rate_model <- mixed(estimate ~ age_z * valence * agency + (1|subject_id),
                             data = learning_rates,
                             method = "S")
learning_rate_model
summary(learning_rate_model)
# main effect of age
# main effect of valence
# valence x agency interaction


#t test between alpha pos choice and alpha neg choice
t.test(model_params$alphaPosChoice, model_params$alphaNegChoice, paired = T)
#significant

#t test between alpha pos comp and alpha neg comp
t.test(model_params$alphaPosComp, model_params$alphaNegComp, paired = T)
#not significant

```


# Plot relations between model parameters and age
```{r age parameter plot, fig.width = 7, fig.height = 4, units = "in"}

params_long <- model_params %>%
  pivot_longer(names_to = "param",
               values_to = "estimate",
               cols = c(alphaPosChoice:agencyBonus)) 

params_long$param <- factor(params_long$param, 
                            levels = c("alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"),
                            labels = c(TeX("$\\alpha_{choice_+}$"), 
                                       TeX("$\\alpha_{choice_-}$"), 
                                       TeX("$\\alpha_{comp_+}$"), 
                                       TeX("$\\alpha_{comp_-}$"), 
                                       TeX("$\\beta_{agency}$"), 
                                       TeX("$\\beta_{machine}$"),
                                       "Agency~Bonus"
                            ))

params_plot <- ggplot(params_long, aes(x = age, y = estimate, color = param)) +
  facet_wrap(~param, scale = "free", labeller = label_parsed, nrow = 2) +
  geom_point() +
  geom_smooth(method = "lm", aes(fill = param)) +
  ylab("Parameter Estimate") +
  xlab("Age") +
  voc_theme() +
  theme(legend.position = "none")
params_plot
```


